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The science of deep learning.

作者信息

Baraniuk Richard, Donoho David, Gavish Matan

机构信息

Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005.

Department of Statistics, Stanford University, Stanford, CA 94305;

出版信息

Proc Natl Acad Sci U S A. 2020 Dec 1;117(48):30029-30032. doi: 10.1073/pnas.2020596117. Epub 2020 Nov 23.

DOI:10.1073/pnas.2020596117
PMID:33229565
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7720210/
Abstract
摘要

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本文引用的文献

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Understanding the role of individual units in a deep neural network.理解深度神经网络中单个单元的作用。
Proc Natl Acad Sci U S A. 2020 Dec 1;117(48):30071-30078. doi: 10.1073/pnas.1907375117. Epub 2020 Sep 1.
2
Fast reinforcement learning with generalized policy updates.快速强化学习与广义策略更新。
Proc Natl Acad Sci U S A. 2020 Dec 1;117(48):30079-30087. doi: 10.1073/pnas.1907370117. Epub 2020 Aug 17.
3
Algorithms as discrimination detectors.算法作为歧视探测器。
Proc Natl Acad Sci U S A. 2020 Dec 1;117(48):30096-30100. doi: 10.1073/pnas.1912790117. Epub 2020 Jul 28.
4
Theoretical issues in deep networks.深度网络中的理论问题。
Proc Natl Acad Sci U S A. 2020 Dec 1;117(48):30039-30045. doi: 10.1073/pnas.1907369117. Epub 2020 Jun 9.
5
Emergent linguistic structure in artificial neural networks trained by self-supervision.自我监督训练的人工神经网络中的紧急语言结构。
Proc Natl Acad Sci U S A. 2020 Dec 1;117(48):30046-30054. doi: 10.1073/pnas.1907367117. Epub 2020 Jun 3.
6
The frontier of simulation-based inference.基于模拟的推断前沿。
Proc Natl Acad Sci U S A. 2020 Dec 1;117(48):30055-30062. doi: 10.1073/pnas.1912789117. Epub 2020 May 29.
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On instabilities of deep learning in image reconstruction and the potential costs of AI.深度学习在图像重建中的不稳定性及人工智能的潜在代价
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The unreasonable effectiveness of deep learning in artificial intelligence.深度学习在人工智能中取得的不合理成效。
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